Honors Biology

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Qualitative vs. Quantitative Data

• Honors Biology
• Mr. Luis A. Velázquez

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Qualitative vs. Quantitative Data

• Quantitative data is information about
  quantities that is, information that can be
  measured and written down with numbers.
• Qualitative data is information about qualities
  information that can't actually be measured.

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Qualitative vs. Quantitative Data

• The age of your car.  
• The number of hairs on your knuckle.  
• The softness of a cat.  
• The color of the sky. 
• The number of pennies in your pocket.

(Quantitative.)
(Quantitative.)
(Qualitative.)
(Qualitative.)
(Quantitative.)
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Categorical Data

• This is data that can be organized into mutually
  exclusive categories.
• If we look at a bunch of bananas and they're all
  either green, brown, yellow or blue, then we
  could use the categories "green," "brown,"
  "yellow" and "blue" to record our data.
• Categorical data is usually qualitative. However,
  quantitative data can also be put into
  categories.

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Raw Data

• Unanalyzed data data not yet subjected to
  analysis.
• Raw data is never is use in a graph.
• Also known as primary data.

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Raw Data

• according to statistics, the details given by
  investigator or collected from sources are known
  as raw data.
• In other words its the first hand information
  undergone no mathematical or statistical
  treatment also called as raw data.

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Results vs. Conclusion

• Conclusion and Results are two terms used in
  thesis writing and surveys or experiments
  respectively.
• Conclusion forms the end part of a thesis or a
  dissertation.
• Results form the end part of a survey or a
  chemical experiment. This is one of the main
  differences between conclusion and results.
• Read more http//www.differencebetween.com/diff
  erence-between-conclusion-and-vs-results/ixzz2fMS
  oh3ER

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• Conclusion aims at the briefing of the research
  findings of the researcher. It should be short
  and concise.
• It should contain concise and short paragraphs.
• A conclusion should not contain long paragraphs.
• Results can be statistical in composition and
  sometimes descriptive too. If they are
  descriptive in nature then they can contain long
  paragraphs.
• Read more http//www.differencebetween.com/diff
  erence-between-conclusion-and-vs-results/ixzz2fMT
  S2L5q

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Null Hypothesis

• The simplistic definition of the null is as the
  opposite of the alternative hypothesis.
• The null hypothesis (H0) is a hypothesis which
  the researcher tries to disprove, reject or
  nullify.
• The 'null' often refers to the common view of
  something.
• The alternative hypothesis is what the researcher
  really thinks is the cause of a phenomenon.
• Read more http//explorable.com/null-hypothesis
  

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• An experiment conclusion always refers to the
  null, rejecting or accepting H0 rather than H1.
• Despite this, many researchers neglect the null
  hypothesis when testing hypotheses, which is poor
  practice and can have adverse effects.
• Read more http//explorable.com/null-hypothesis
  

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• A researcher may postulate a hypothesis
• H1 Tomato plants exhibit a higher rate of growth
  when planted in compost rather than in soil.
• And a null hypothesis
• H0 Tomato plants do not exhibit a higher rate of
  growth when planted in compost rather than soil.